IEEE Communications Surveys and Tutorials最新文献

{"title":"A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications, and Challenges","authors":"Feibo Jiang;Cunhua Pan;Li Dong;Kezhi Wang;Merouane Debbah;Dusit Niyato;Zhu Han","doi":"10.1109/COMST.2026.3660844","DOIUrl":"10.1109/COMST.2026.3660844","url":null,"abstract":"The 6G wireless communications aim to establish an intelligent world of ubiquitous connectivity, providing an unprecedented communication experience. Large artificial intelligence models (LAMs) are characterized by significantly larger scales (e.g., billions or trillions of parameters) compared to typical artificial intelligence (AI) models. LAMs exhibit outstanding cognitive abilities, including strong generalization capabilities for fine-tuning to downstream tasks, and emergent capabilities to handle tasks unseen during training. Therefore, LAMs efficiently provide AI services for diverse communication applications, making them crucial tools for addressing complex challenges in future wireless communication systems. This study provides a comprehensive review of the foundations, applications, and challenges of LAMs in communication. First, we introduce the current state of AI-based communication systems, emphasizing the motivation behind integrating LAMs into communications and summarizing the key contributions. We then present an overview of the essential concepts of LAMs in communication. This includes an introduction to the main architectures of LAMs, such as transformer, diffusion models, and mamba. We also explore the classification of LAMs, including large language models (LLMs), large vision models (LVMs), large multimodal models (LMMs), and world models, and examine their potential applications in communication. Additionally, we cover the training methods and evaluation techniques for LAMs in communication systems. Lastly, we introduce optimization strategies such as chain of thought (CoT), retrieval augmented generation (RAG), and agentic systems. Following this, we discuss the research advancements of LAMs across various communication scenarios, including physical layer design, resource allocation and optimization, network design and management, edge intelligence, semantic communication, agentic systems, and emerging applications. Finally, we analyze the major challenges in current research, including the lack of high-quality and structured communication data, hallucination and limited reasoning in generative models, poor explainability and adaptability, deployment under resource constraints, as well as security and privacy risks, and provide insights into potential future directions.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4731-4764"},"PeriodicalIF":34.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches","authors":"Xiaozheng Gao;Yichen Wang;Bosen Liu;Xiao Zhou;Ruichen Zhang;Jiacheng Wang;Dusit Niyato;Dong In Kim;Abbas Jamalipour;Chau Yuen;Jianping An;Kai Yang","doi":"10.1109/COMST.2026.3660854","DOIUrl":"10.1109/COMST.2026.3660854","url":null,"abstract":"The development of satellite-augmented low-altitude economy and terrestrial networks (SLAETNs) demands intelligent and autonomous systems that can operate reliably across heterogeneous, dynamic, and mission-critical environments. To address these challenges, this article surveys the state-of-the-art literature on enabling agentic artificial intelligence (AI), that is, artificial agents capable of perceiving, reasoning, and acting, through the application of generative AI (GAI) and large language models (LLMs) in SLAETNs. We begin by introducing the architecture and characteristics of SLAETNs, analyzing the challenges that arise in integrating satellite, aerial, and terrestrial components. Then, we present a model-driven foundation by systematically reviewing five major categories of generative models: variational autoencoders (VAEs), generative adversarial networks (GANs), generative diffusion models (GDMs), transformer-based models (TBMs), and LLMs. Moreover, we discuss critical deployment strategies and propose a conceptual framework for deploying agentic AI in SLAETNs. Building on this foundation, we examine how these models empower agentic functions across three domains: communication enhancement, security and privacy protection, and intelligent satellite tasks. Moreover, we present a comprehensive guide to open-source frameworks, datasets, and simulation platforms, and deliver a case study on agentic network optimization in SLAETN scenarios. Finally, we outline key future directions for building scalable, adaptive, and trustworthy generative agents in SLAETNs. This survey aims to provide a unified understanding and actionable reference for advancing agentic AI in next-generation networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4800-4841"},"PeriodicalIF":34.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"6G AI Security: From Fundamentals to Offensive and Defensive Landscape in 6G","authors":"An Braeken;Diana Deac;Thanh Linh Nguyen;Gürkan Gür;Quoc-Viet Pham;Charithri Yapa;Paola G. Vinueza-Naranjo;Henry Carvajal Mora;Charuka Moremada;Madhusanka Liyanage","doi":"10.1109/COMST.2026.3659793","DOIUrl":"10.1109/COMST.2026.3659793","url":null,"abstract":"The sixth generation (6G) of mobile networks presents transformative possibilities but also introduces significant security challenges. This paper surveys the state-of-the-art in 6G security from 2020 to 2025, highlighting advancements, challenges, and potential solutions. We distinguish our survey by offering a comprehensive, cross-layer analysis of the role of artificial intelligence (AI) in both enabling and threatening 6G security. First, we outline the technological foundations and core features of 6G and 6G AI, emphasizing their implications for security. We then explore how AI can be leveraged for both offensive and defensive purposes across the infrastructure, network service, and application layers. Our study categorizes AI-driven threats and countermeasures and provides a structured analysis of five key security frameworks. We further examine how quantum-enhanced technologies contribute to securing AI in 6G environments. Finally, we identify open research questions and present key lessons learned to guide future research in building resilient and trustworthy AI-integrated 6G networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4765-4799"},"PeriodicalIF":34.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Sensing and Communications Over the Years: An Evolution Perspective","authors":"Di Zhang;Yuanhao Cui;Xiaowen Cao;Nanchi Su;Yi Gong;Fan Liu;Weijie Yuan;Xiaojun Jing;J. Andrew Zhang;Jie Xu;Christos Masouros;Dusit Niyato;Marco Di Renzo","doi":"10.1109/COMST.2026.3655674","DOIUrl":"10.1109/COMST.2026.3655674","url":null,"abstract":"Integrated sensing and communications (ISAC) enables efficient spectrum utilization and reduces hardware costs for beyond 5G (B5G) and 6G networks, facilitating intelligent applications that require both high-performance communication and precise sensing capabilities. This survey provides a comprehensive review of the evolution of ISAC over the years. We examine the expansion of spectrum across radio-frequency (RF) and optical ISAC, highlighting the role of advanced technologies, along with key challenges and synergies. We further discuss the advancements in network architecture from single-cell to multi-cell systems, emphasizing the integration of collaborative sensing and interference mitigation strategies. Moreover, we analyze the progress from single-modal to multi-modal sensing, with a focus on the integration of edge intelligence to enable real-time data processing, reduce latency, and enhance decision-making. Finally, we extensively review standardization efforts by 3GPP, IEEE, and ITU, examining the transition of ISAC-related technologies and their implications for the deployment of 6G networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5014-5048"},"PeriodicalIF":34.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Tutorial on SDR-Based NB-IoT PHY: Synchronization, Demodulation, and Validation","authors":"Jingze Zheng;Zhiguo Shi;Xiuzhen Guo;Shibo He;Chaojie Gu;Jiming Chen","doi":"10.1109/COMST.2026.3654924","DOIUrl":"10.1109/COMST.2026.3654924","url":null,"abstract":"Low-Power Wide-Area Networks (LPWANs) have become fundamental to the Internet of Things (IoT), with NB-IoT (Narrowband Internet of Things) standing out due to its seamless integration with cellular infrastructure, enhanced coverage, and support for dense deployments. Despite its commercial proliferation, SDR-based physical layer (PHY) exploration for NB-IoT remains limited, particularly in addressing unique complexities such as narrowband signal processing, cellular-specific synchronization sequences, and stringent link budget requirements. This paper bridges this gap by presenting a comprehensive tutorial on SDR-based NB-IoT PHY implementation, focusing on three pillars: robust time-frequency synchronization under severe fading and interference, efficient channel estimation for coherent detection, and experimental performance validation in real-world scenarios. We introduce a first-of-its-kind end-to-end SDR implementation supporting both single-tone and multi-tone transmissions, leveraging commercial off-the-shelf (COTS) platforms. Our novel signal processing workflow achieves synchronization through NPSS-based auto-correlation and NSSS-driven cell-ID detection while incorporating CFO estimation and compensation to mitigate oscillator mismatches. For uplink processing, we detail preamble detection and demodulation, addressing coverage enhancement (CE) levels and adaptive subcarrier spacing configurations. Extensive experiments conducted in both indoor (LOS/NLOS) and outdoor environments demonstrate reliable performance, with Bit Error Rate (BER) and Block Error Rate (BLER) metrics validating resilience under varying repetition counts and propagation conditions. The tutorial offers actionable insights for optimizing PHY-layer design, validated against 3GPP specifications, and lays the foundation for next-generation NB-IoT systems in emerging applications, such as smart cities and industrial automation.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4458-4484"},"PeriodicalIF":34.4,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions","authors":"Ruichen Zhang;Guangyuan Liu;Yinqiu Liu;Changyuan Zhao;Jiacheng Wang;Yunting Xu;Dusit Niyato;Jiawen Kang;Yonghui Li;Shiwen Mao;Sumei Sun;Xuemin Shen;Dong In Kim","doi":"10.1109/COMST.2026.3651702","DOIUrl":"10.1109/COMST.2026.3651702","url":null,"abstract":"The rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralized edge general intelligence. However, traditional edge intelligence methods, characterized by static models and limited cognitive autonomy, fail to address the dynamic, heterogeneous, and resource-constrained scenarios inherent to emerging edge networks. Agentic artificial intelligence (Agentic AI) emerges as a transformative solution, enabling edge systems to autonomously perceive multi-modal environments, reason contextually, and adapt proactively through continuous perception–reasoning–action loops. In this context, the agentification of edge intelligence serves as a key paradigm shift, where distributed entities evolve into autonomous agents capable of collaboration and continual adaptation. This paper presents a comprehensive survey dedicated to Agentic AI and agentification frameworks tailored explicitly for edge general intelligence. First, we systematically introduce foundational concepts and clarify distinctions from traditional edge intelligence paradigms. Second, we analyze important enabling technologies, including compact model compression, energy-aware computing strategies, robust connectivity frameworks, and advanced knowledge representation and reasoning mechanisms. Third, we provide representative case studies demonstrating Agentic AI’s capabilities in low-altitude economy networks, intent-driven networking, vehicular networks, and human-centric service provisioning, supported by numerical evaluations. Furthermore, we identify current research challenges, review emerging open-source platforms, and highlight promising future research directions to guide robust, scalable, and trustworthy Agentic AI deployments for next-generation edge environments.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4285-4318"},"PeriodicalIF":34.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges, and Future Research","authors":"Yuan Gao;Zichen Lu;Xinyi Wu;Wenjun Yu;Shengli Liu;Jianbo Du;Yanliang Jin;Shunqing Zhang;Xiaoli Chu;Shugong Xu","doi":"10.1109/COMST.2026.3652799","DOIUrl":"10.1109/COMST.2026.3652799","url":null,"abstract":"The acquisition of channel state information (CSI) plays a vital role in enhancing the performance of sixth-generation (6G) wireless communication systems. Conventional channel estimation approaches encounter significant scalability limitations in emerging scenarios, such as high-mobility environments, extremely large-scale multiple-input multiple-output (XL-MIMO) configurations, and multi-band operations, where pilot overhead grows dramatically. CSI extrapolation offers an effective solution to these issues by leveraging limited or partial CSI measurements to reconstruct or predict the full CSI, thereby substantially lowering the required overhead without compromising accuracy. Artificial intelligence (AI) has emerged as a powerful tool to advance CSI extrapolation, enabling more accurate and efficient inference across diverse channel conditions. Although research in this area is expanding rapidly, the literature still lacks a thorough and unified survey that synthesizes the latest developments in AI-based CSI extrapolation methods. This paper aims to bride this gap by providing the first comprehensive review of AI-driven CSI extrapolation techniques, covering their current state, key limitations, and promising research avenues. We begin by outlining the foundational aspects of AI-driven CSI extrapolation. This includes essential wireless channel properties that influence extrapolation performance and an overview of the most commonly employed AI architectures suited to this task. Building on these basics, we systematically examine the major categories of extrapolation approaches, both traditional model-based and modern AI-enhanced ones, across the primary domains: time, frequency, antenna, and multi-domain scenarios. For each category, we highlight representative techniques, their underlying principles, strengths, and limitations, along with distilled insights from comparative studies. Recognizing the strong potential of AI-based methods to satisfy the demanding performance targets of future systems, we also review publicly available open channel datasets and channel simulators that support the development and benchmarking of robust AI-driven extrapolation models. Finally, we identify persistent challenges in the field, and outline forward-looking research directions to guide future progress toward practical deployment in 6G networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4485-4518"},"PeriodicalIF":34.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agentic Graph Neural Networks for Wireless Communications and Networking Toward Edge General Intelligence: A Survey","authors":"Yang Lu;Shengli Zhang;Chang Liu;Ruichen Zhang;Bo Ai;Dusit Niyato;Wei Ni;Xianbin Wang;Abbas Jamalipour","doi":"10.1109/COMST.2026.3651990","DOIUrl":"10.1109/COMST.2026.3651990","url":null,"abstract":"The rapid advancement of communication technologies has driven the evolution of communication networks toward both high-dimensional resource utilization and multifunctional integration. This evolving complexity poses significant challenges in designing communication networks to satisfy the growing quality-of-service and time sensitivity of mobile applications in dynamic environments. Graph neural networks (GNNs) have emerged as fundamental deep learning (DL) models for complex communication networks. Most existing GNNs are task-specific, whereas end-to-end communication performance hinges on multi-step inference. To address this gap, this article proposes to leverage agentic artificial intelligence (AI) to orchestrate and integrate diverse GNNs, thereby forming a novel framework termed agentic GNNs. This framework enables application-aware implementations, facilitating the advancement of edge general intelligence. Regarding the core roles of GNNs in the framework, we comprehensively review recent advances in GNN-based applications for wireless communications and networking, aiming to fully understand the comprehensive capabilities of GNNs. Specifically, we focus on the alignment between graph representations and network topologies, as well as between neural architectures and communication tasks. We first provide an overview of GNNs based on prominent neural architectures, followed by the concept of agentic GNNs. Then, we summarize and compare GNN applications for conventional systems and emerging technologies, including physical, MAC, and network layer designs, integrated sensing and communication (ISAC), reconfigurable intelligent surface (RIS) and cell-free network architecture. We further propose a large language model (LLM) framework as an intelligent question-answering agent, leveraging this survey as a local knowledge base to enable GNN-related responses tailored to wireless communication research. Moreover, we present several experimental results to quantify the effectiveness of GNNs across various scenarios. Finally, we highlight the critical challenges, open issues, and future research directions for GNN-empowered wireless communication designs.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4519-4554"},"PeriodicalIF":34.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Tutorial on MIMO-OFDM ISAC: From Far-Field to Near-Field","authors":"Qianglong Dai;Yong Zeng;Huizhi Wang;Changsheng You;Chao Zhou;Hongqiang Cheng;Xiaoli Xu;Shi Jin;A. Lee Swindlehurst;Yonina C. Eldar;Robert Schober;Rui Zhang;Xiaohu You","doi":"10.1109/COMST.2025.3650568","DOIUrl":"10.1109/COMST.2025.3650568","url":null,"abstract":"Integrated sensing and communication (ISAC) is one of the key usage scenarios for future sixth-generation (6G) mobile communication networks, where communication and sensing (C&S) services are simultaneously provided through shared wireless spectrum, signal processing modules, hardware, and network infrastructure. Such an integration is strengthened by the technology trends in 6G, such as denser network nodes, larger antenna arrays, wider bandwidths, higher frequency bands, and more efficient utilization of spectrum and hardware resources, which incentivize and empower enhanced sensing capabilities. Moreover, emerging applications such as Internet-of-Everything (IoE), autonomous ground and aerial vehicles, virtual reality/augmented reality (VR/AR), and connected intelligence have intensified the demands for both high-quality C&S services, accelerating the development and implementation of ISAC in wireless networks. As in contemporary communication systems, orthogonal frequency-division multiplexing (OFDM) is expected to be the dominant waveform for ISAC, motivating the need for study of both the potential benefits and challenges of OFDM ISAC. Thus, this paper aims to provide a comprehensive tutorial overview of ISAC systems enabled by large-scale multi-input multi-output (MIMO) and OFDM technologies and discuss their fundamental principles, advantages, and enabling signal processing methods. To this end, a unified MIMO-OFDM ISAC system model is first introduced, followed by four frameworks for estimating parameters across the spatial, delay, and Doppler domains, including parallel one-domain, sequential one-domain, joint two-domain, and joint three-domain parameter estimation. Next, sensing algorithms and performance analysis are presented in detail for far-field scenarios where uniform plane wave (UPW) propagation is valid, followed by extensions to near-field scenarios where uniform spherical wave (USW) characteristics must be considered. Finally, the paper presents open challenges and outlines promising avenues for future research on MIMO-OFDM ISAC.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4319-4358"},"PeriodicalIF":34.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconfigurable Antennas for Next-Generation Mobile Communication Networks: A Comprehensive Survey and Tutorial","authors":"Yizhe Zhao;Long Zhang;Halvin Yang;Kun Yang;Rui Zhang;Lingyang Song;Yuanwei Liu","doi":"10.1109/COMST.2026.3673688","DOIUrl":"10.1109/COMST.2026.3673688","url":null,"abstract":"The transition to next-generation mobile communication networks, particularly 6G, demands advanced technologies to meet the requirements for ultra-reliable, low-latency communication, massive connectivity, and intelligent applications. Reconfigurable antennas (RAs) play a crucial role in achieving these objectives by enabling dynamic adjustments to the radio frequency (RF) characteristics of antennas, such as gain, radiation pattern, impedance, and polarization. Unlike traditional fixed-position antennas, RAs can alter both their radiation patterns and positions, offering flexibility in response to varying communication environments. This paper presents a comprehensive survey and tutorial on RAs, with a focus on fluid antennas (FAs), movable antennas (MAs), pinching antennas (PAs), and reconfigurable holographic antennas (RHAs), examining their potential in next-generation mobile networks. We explore the channel modelling and estimation, performance analysis, resource allocation strategies, and their synergy with other emerging wireless technologies for each type of RA. Finally, we provide a comparative analysis of different RAs and discuss the open challenges and future research directions, offering insights and guidance for future investigations in the exciting research area.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5267-5306"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147454469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}